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We introduce a new method for processing agents in agent-based models that significantly improves the efficiency of certain models. Dynamic Agent Compression allows agents to shift in and out of a compressed state based on their changing levels of heterogeneity. Sets of homogeneous agents are stored in compact bins, making the model more efficient in its(More)
We describe the spatial Agent-Based Computational Laboratory that we have developed to study the pandemic influenza risks of US cities. This research presented a series of interesting challenges, from the uncertainty surrounding the future epidemiological characteristics of a human-transmission H5N1 strain of pandemic influenza, to the need to provide(More)
We introduce a new method for processing agents in agent-based models that significantly improves the efficiency of certain models. Dynamic Agent Compression allows agents to shift in and out of a compressed state based on their changing levels of heterogeneity. Sets of homogeneous agents are stored in compact bins, making the model more efficient in its(More)
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